# femind
A standalone Rust crate providing a pluggable, feature-gated memory engine for AI agent applications.
Handles persistent storage, keyword search (FTS5), vector search (candle), hybrid retrieval (RRF), graph relationships, memory consolidation, cognitive decay modeling, and token-budget-aware context assembly.
## Design Principles
- **Library, not framework** — projects call into femind, not the other way around
- **Feature-gated everything** — heavy dependencies behind compile-time flags
- **Local-first** — SQLite-backed, single-file databases, no cloud dependency
- **Pure Rust where possible** — candle over ort, SQLite over Postgres
- **Proven patterns only** — every component backed by research or established practice
## Status
The local crate and repo are now `femind` / `fe-mind`. The package rename is complete locally, and publication work is the remaining external packaging step. Non-LLM verification is currently green:
- `cargo test`
- `cargo test --features full`
- `cargo clippy --all-targets --all-features -- -D warnings`
The practical live-validation path is now established and repeatable:
- recommended API extraction default: DeepInfra `openai/gpt-oss-120b`
- recommended CLI extraction default: Codex CLI `gpt-5.4-mini`
- lower-cost CLI fallback: Codex CLI `gpt-5.1-codex-mini`
- retrieval-only practical eval in `exact` mode currently passes `9/9`
- retrieval-only practical eval in `ann` mode currently passes `9/9`
- broader live-usage sample built from actual project docs currently passes `11/11`
- standard local runner: `scripts/run-practical-eval.sh`
## Migration
`femind` is the successor to the earlier `mindcore` crate and repository.
The published `mindcore` crate remains the legacy package line; new work and
future releases should target `femind`.
The next remaining work is release packaging and publication. See:
- `ARCHITECTURE.md` — full crate structure and API design
- `RESEARCH.md` — research, landscape analysis, and specification
- `DECISIONS.md` — architectural decisions log
- `PRACTICAL_EVAL.md` — real-world validation plan and practical eval categories
- `eval/practical/` — curated practical validation scenarios
- `research/` — competitive landscape analysis